We introduce ABC-Dataset, a collection of one million Computer-Aided Design
(CAD) models for research of geometric deep learning methods and applications.
Each model is a collection of explicitly parametrized curves and surfaces,
providing ground truth for differential quantities, patch segmentation,
geometric feature detection, and shape reconstruction. Sampling the parametric
descriptions of surfaces and curves allows generating data in different formats
and resolutions, enabling fair comparisons for a wide range of geometric
learning algorithms. As a use case for our dataset, we perform a large-scale
benchmark for estimation of surface normals, comparing existing data driven
methods and evaluating their performance against both the ground truth and
traditional normal estimation methods.